LLM Fine-Tuning Engineer
Tasks
- Build distributed training pipelines
- Build instruction tuning datasets
- Collaborate on fine tuning roadmaps
- Design evaluation suites with benchmarks and human evaluation
- Design fine tuning experiments for large language models
- Document training methods and results
- Implement model safety and policy evaluations
- Implement parameter efficient fine tuning
- Manage model artifacts and experiment reproducibility
- Mentor engineers on fine tuning and evaluation
- Operate GPU cluster training jobs
- Optimize training throughput with mixed precision and attention optimizations
- Tune hyperparameters and optimizer configurations
Perks/Benefits
Skills/Tech-stack
Adapter methods | Attention Optimization | Benchmarking | Dataset Quality Assurance | Dataset curation | Dataset quality | Direct Preference Optimization | Distributed Training | Efficient Fine Tuning | Evaluation | Experiment tracking | FSDP | Fine Tuning | GPU Computing | Human Feedback | Human evaluation | Hyperparameter Tuning | Language Models | Large Language Models | Learning from Human Feedback | LoRA | Machine Learning | Mixed Precision | Parameter efficient fine-tuning | Pipeline parallelism | Policy Evaluation | Preference optimization | PyTorch | Python | QLoRA | Quality Assurance | Reinforcement Learning | Reinforcement Learning from Human Feedback | Reproducibility | Safety evaluation | Sequence Packing | Supervised Learning | Zero
Education
Roles
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